부산대학교 도서관

Optical flow plays an important role in many applications, such as autonomous driving, where it facilitates the perception and understanding of the surrounding environment. Since the rise of deep learning approaches for optical flow estimation, much effort has been expended on new architectures and on increasing the accuracy of predicted flow. However, topics such as including semantic knowledge in model training or estimating the uncertainty for the predicted optical flow have received comparatively little attention so far. In this paper we investigate the effect of adding semantic supervision for a state of the art supervised optical flow model [1], as well as the necessary adaptations needed for obtaining a measure of flow uncertainty. In addition, our method is suitable for deployment and integration on various embedded hardware platforms for automated driving. In our experiments, we find that semantic guidance is able to improve the model performance on the KITTI benchmark by 9% on foreground objects, after having been trained only on synthetic data from the target domain. We also obtain improved flow uncertainty estimates over commonly used approaches such as model ensembles and Monte Carlo Dropout.